# US 10,311,566 B2 · Methods and systems for automatically determining image characteristics serving as a basis for a diagnosis associated with an image study type

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- Number: US 10,311,566 B2
- Document ID: US10311566B2
- Status: Granted
- Kind: B2
- Application number: 15/179,448
- Filed: 2016-06-10
- Published: 2016-12-15
- Granted: 2019-06-04
- Earliest claimed: 2015-06-12
- Assignees: International Business Machines Corporation
- Applicants: International Business Machines Corporation
- Inventors: Murray A. Reicher; Jon T. DeVries; Michael W. Ferro; Marwan Sati
- Web page: https://patentgrep.com/patent/US10311566B2

## CPC

- G06T 7/0012: {Biomedical image inspection}
- A61B 5/055: involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
- A61B 5/7267: {involving training the classification device}
- A61B 10/02: Instruments for taking cell samples or for biopsy {(A61B10/0038 and A61B10/0045 take precedence; needle locating or guiding means A61B17/3403; samplers for enzymology or microbiology C12M1/26; sampling or preparing biological specimens G01N33/48)}
- A61B 34/10: Computer-aided planning, simulation or modelling of surgical operations
- G06F 3/0482: Interaction with lists of selectable items, e.g. menus
- G06F 3/0488: using a touch-screen or digitiser, e.g. input of commands through traced gestures
- G06F 18/214: Generating training patterns; Bootstrap methods, e.g. bagging or boosting
- G06F 18/217: {Validation; Performance evaluation; Active pattern learning techniques}
- G06F 18/2411: based on the proximity to a decision surface, e.g. support vector machines
- G06F 18/2413: based on distances to training or reference patterns
- G06F 18/285: {Selection of pattern recognition techniques, e.g. of classifiers in a multi-classifier system}
- G06F 40/169: Annotation, e.g. comment data or footnotes
- G06F 40/205: Parsing
- G06N 20/00: Machine learning
- G06T 7/0014: {using an image reference approach}
- G06T 12/30: Image post-processing, e.g. metal artefact correction
- G06V 10/764: using classification, e.g. of video objects
- G16H 10/20: for electronic clinical trials or questionnaires
- G16H 15/00: ICT specially adapted for medical reports, e.g. generation or transmission thereof
- G16H 20/10: relating to drugs or medications, e.g. for ensuring correct administration to patients
- G16H 30/20: for handling medical images, e.g. DICOM, HL7 or PACS
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- G16H 50/50: for simulation or modelling of medical disorders
- G16Z 99/00: Subject matter not provided for in other main groups of this subclass
- A61B 5/0077: {Devices for viewing the surface of the body, e.g. camera, magnifying lens}
- A61B 5/015: {By temperature mapping of body part}
- A61B 5/4312: {Breast evaluation or disorder diagnosis (optical mammography A61B5/0091)}
- A61B 5/441: {Skin evaluation, e.g. for skin disorder diagnosis}
- A61B 2034/108: {Computer aided selection or customisation of medical implants or cutting guides}
- A61B 2090/373: {using light, e.g. by using optical scanners}
- A61B 2090/374: {NMR or MRI}
- A61B 2090/3762: {using computed tomography systems [CT]}
- A61B 2090/378: {using ultrasound}
- G06T 2200/24: involving graphical user interfaces [GUIs]
- G06T 2207/10088: Magnetic resonance imaging [MRI]
- G06T 2207/20081: Training; Learning
- G06T 2207/30004: Biomedical image processing
- G06T 2207/30008: Bone
- G06T 2207/30016: Brain
- G06T 2207/30052: Implant; Prosthesis
- G06T 2207/30061: Lung
- G06T 2207/30068: Mammography; Breast
- G06T 2207/30088: Skin; Dermal
- G06V 2201/031: of internal organs
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16Z: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS, NOT OTHERWISE PROVIDED FOR

## Abstract

Methods and systems for automatically determining image characteristics serving as a basis for a diagnosis associated with an image study type. One system includes a server including an electronic processor and an interface for communicating with at least one data source. The electronic processor is configured to receive an image study from the at least one data source over the interface. The image study being of the image study type and including a plurality of images. The electronic processor is also configured to determine an image characteristic for each of the plurality of images and determine whether each of the plurality of images was used to establish a diagnosis. The electronic processor is also configured to store the image characteristic for each of the plurality of images and an indicator of whether each of the plurality of images was used to establish the diagnosis in a data structure.

## Claims

### Claim 1

A system of automatically determining image characteristics used to diagnosis a patient, the image characteristics associated with an image study type, wherein the image study type specifies an imaging procedure used to generate images, the system comprising:
a server including an electronic processor and an interface for communicating with at least one data source, the electronic processor configured to
receive an image study from the at least one data source over the interface, the image study being of the image study type and including a plurality of images,
determine an image characteristic for each of the plurality of images by at least one of a group consisting of determining whether each of the plurality of images includes an annotation and determining whether each of the plurality of images was displayed to a diagnosing physician rendering the diagnosis during a reading session of the diagnosing physician,
determine whether each of the plurality of images was used in diagnosing a patient,
create a data structure and store the image characteristic for each of the plurality of images and an indicator of whether each of the plurality of images was used in diagnosing a patient in the data structure, and
modify a newly received image study of the same image study type as the first image study based upon the data structure by at least one of a group consisting of removing at least one image from the newly received image study that does not contain an image characteristic stored in the data structure and prioritizing images in the newly received image study for review.

### Claim 2 (depends on 1)

The system of claim 1, wherein the image characteristic includes an anatomical structure.

### Claim 3 (depends on 1)

The system of claim 1, wherein the image characteristic includes whether a contrast agent was used or a radioactive isotope was used.

### Claim 4 (depends on 1)

The system of claim 1, wherein the image characteristic includes a time after a contrast agent or a radioactive isotope was introduced.

### Claim 5 (depends on 1)

The system of claim 1, wherein the image characteristic includes an orientation.

### Claim 6 (depends on 1)

The system of claim 1, wherein the image characteristic includes an image acquisition parameter.

### Claim 7 (depends on 1)

The system of claim 1, wherein the image characteristic includes DICOM header information.

### Claim 8 (depends on 1)

The system of claim 1, wherein the annotation includes a graphical marker designating a portion of an image.

### Claim 9 (depends on 1)

The system of claim 1, wherein the annotation includes diagnostic information associated with an image.

### Claim 10 (depends on 1)

The system of claim 1, wherein the electronic processor is configured to determine whether each of the plurality of images was used to establish the diagnosis by determining whether each of the plurality of images was diagnosed as normal.

### Claim 11 (depends on 1)

The system of claim 1, wherein the electronic processor is further configured to automatically determine a frequency of an image with a particular image characteristic included in a subsequent image study of the image study type based on the data structure.

### Claim 12 (depends on 11)

The system of claim 11, wherein the frequency is a frequency of the image with the particular image characteristic including an annotation.

### Claim 13 (depends on 11)

The system of claim 11, wherein the frequency is a frequency of the image with the particular image characteristic not including an annotation.

### Claim 14 (depends on 11)

The system of claim 11, wherein the frequency is a frequency of the image with the particular image characteristic being displayed to a diagnosing physician.

### Claim 15 (depends on 11)

The system of claim 11, wherein the frequency is a frequency of the image with the particular image characteristic not being displayed to a diagnosing physician.

### Claim 16 (depends on 11)

The system of claim 11, wherein the frequency is a frequency of the image with the particular image characteristic being diagnosed as normal.

### Claim 17 (depends on 11)

The system of claim 11, wherein the frequency is a frequency of the image with the particular image characteristic not being diagnosed as normal.

### Claim 18 (depends on 11)

The system of claim 11, wherein the electronic processor is further configured to generate a report based on the frequency.

### Claim 19 (depends on 18)

The system of claim 18, wherein the report includes a plurality of frequencies, wherein each of the plurality of frequencies is associated with a different image characteristic.

### Claim 20 (depends on 18)

The system of claim 18, wherein the plurality of frequencies includes frequencies satisfying at least one threshold.

### Claim 21 (depends on 20)

The system of claim 20, wherein the at least one threshold is configurable based on one or more preferences associated with a user.

### Claim 22 (depends on 18)

The system of claim 18, wherein the report includes plurality of frequencies, wherein each of the plurality of frequencies is associated with a different image study type.

### Claim 23 (depends on 18)

The system of claim 18, wherein the report is parsable by at least one selected from a group consisting of an imaging modality, a diagnosing physician, an anatomical structure, patient demographic information, and a date.

### Claim 24 (depends on 1)

The system of claim 1, wherein the electronic processor is further configured to modify a subsequent image study of the image study type to eliminate at least one image from being generated for the subsequent image study based on the data structure.

### Claim 25 (depends on 1)

The system of claim 1, wherein the electronic processor is further configured to receive a plurality of image studies of the image study type and prioritize the plurality of image studies based on the data structure.